Data analysis method and system for ship repair enterprise operation management

Abstract

The invention provides a data analysis method and system for ship repair enterprise operation management, and belongs to the technical field of data management, and the method specifically comprises the steps: dividing business indexes associated with screening data types into the same business index group, determining a real-time updating data type in the screened data type according to the service indexes in the service index group and the constitution data of the updating demand type in the associated data type, and obtaining a data change condition of the real-time updating data type having a data association relationship with the screened data type, and whether updating processing of the screened data type needs to be carried out is determined based on the data change condition, so that the reliability and the efficiency of updating processing of the operation data of the enterprise are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data management, and in particular relates to a data analysis method and system for operation management of a ship repair enterprise. Background Art

[0002] Enterprises, including ship repair companies, generate various types of data during their operations. Due to the large amount of data and its relatively scattered distribution, there are technical problems such as chaotic data management and inability to obtain effective data in a timely manner.

[0003] In order to realize the analysis and processing of the enterprise's operation management data, the invention patent application CN202411813592.9 "Processing method, device, system and storage medium of enterprise project information data" conducts a series market dynamic analysis on the enterprise project operation data to obtain mathematical model analysis data. Based on the network model analysis data and the mathematical model analysis data, resource utilization analysis is performed to obtain the enterprise operation analysis results. This method can improve the efficiency of enterprise project information data processing, but it has the following technical problems:

[0004] In their daily operations and production, ship repair companies often need to use various types of enterprise operation data to update their operating efficiency indicators. In existing technical solutions, enterprise operation data is often determined by manual filling, which makes it difficult to ensure the real-time and reliability of the update of enterprise operation data. Therefore, how to generate differentiated update processing methods based on the update processing requirements of the enterprise's operating efficiency indicators associated with different types of enterprise operation data to ensure data reliability has become a technical problem that needs to be solved urgently.

[0005] In order to solve the above technical problems, the present application provides a data analysis method and system for operation and management of ship repair enterprises. Summary of the Invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] Specifically, in a first aspect, the present application provides a data analysis method for ship repair enterprise operation management, specifically comprising:

[0008] S1 determines, based on the data type of the enterprise operation data, a related maintenance process of the enterprise operation data in the ship maintenance process, and determines a screening data type in the data type of the enterprise operation data based on a ship maintenance plan and the related maintenance process within a preset future time period;

[0009] S2: taking data types that have a data association relationship with the screened data type as associated data types, and determining the update requirement type in the associated data types based on the associated business indicators of different associated data types;

[0010] S3: classifying the business indicators associated with the screened data type into the same business indicator group, and determining the real-time update data type in the screened data type based on the business indicators in the business indicator group and the constituent data of the update requirement type in the associated data type;

[0011] S4: obtaining data change information of a real-time update data type that has a data association relationship with the filter data type, and determining whether update processing of the filter data type is required based on the data change information.

[0012] The beneficial effects of the present invention are:

[0013] The real-time update data type in the screening data type is determined based on the business indicators in the business indicator group and the constituent data of the update requirement type in the associated data type. This takes into account the differences in the number of business indicator calculations and processes performed through the screening data type and the differences in the requirements for real-time updates of the screening data type. At the same time, it also takes into account the differences in the number of update requirement types in the associated data type, resulting in differences in the requirements for real-time updates of the screening data type during data comparison and verification processing of the associated data type. This enables the identification of screening data types with higher requirements for real-time updates from multiple angles.

[0014] Whether the filtering data type needs to be updated is determined based on the data change of the real-time updated data type that has a data association relationship with the filtering data type, thereby avoiding the technical problem of excessive difficulty in updating all the filtering data types caused by real-time update processing. By judging the data change of the real-time updated data type that has an associated relationship, the filtering data type with a higher probability of data change is screened, thereby ensuring the real-time and reliability of the data of the filtering data type on the basis of reducing the difficulty of update processing.

[0015] A further technical solution is that the data types of the enterprise operation data include plate procurement data, plate collection data, welding rod procurement data, welding rod collection data, and water consumption.

[0016] A further technical solution is that the associated maintenance process is a maintenance process in which the data type of the enterprise operation data changes during the maintenance process.

[0017] A further technical solution is that the method for determining the screening data type in the data type of the enterprise operation data is:

[0018] Determining distribution data of associated maintenance processes of the data type within a future forecast period based on a ship maintenance processing plan within a future preset period;

[0019] Based on the distribution data of the associated maintenance process of the data type, the date on which the associated maintenance process exists is used as the associated date;

[0020] Whether the data type is a screening data type is determined based on the proportion of the number of associated dates within a future preset time period.

[0021] A further technical solution is to determine that the data type is a screening data type when the proportion of the number of associated dates in a future preset time period is greater than a preset date proportion threshold.

[0022] A further technical solution is that, when the data type does not belong to the screened data type, data update processing of the data type is performed by manual statistics.

[0023] A further technical solution is to determine whether it is necessary to update the filtered data type, specifically including:

[0024] Obtain data changes of real-time update data types that have a data association relationship with the filtered data type, and determine whether to update the filtered data type based on the data changes

[0025] The real-time update data type that has a data association relationship with the screening data type is set as the associated update data type;

[0026] Based on the data change of the associated updated data type, performing different data change amounts of the associated updated data type;

[0027] Whether the update process of the filtered data type needs to be performed is determined based on the data variation of different associated update data types.

[0028] A further technical solution is to determine whether the update processing of the filtered data type is required based on the data variation of different associated update data types, specifically including:

[0029] When there is an associated updated data type whose data variation is greater than a preset data variation, it is determined that the update process of the filtered data type needs to be performed.

[0030] A further technical solution is that, when it is determined that the filtered data type needs to be updated, an Internet of Things monitoring device is used to update the data of the filtered data type.

[0031] In a second aspect, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned data analysis method for operation and management of a ship repair enterprise when running the computer program.

[0032] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings.

[0035] Figure 1 It is a flow chart of a data analysis method for the operation and management of a ship repair enterprise;

[0036] Figure 2 A flowchart of a method for determining a screening data type among data types of enterprise operation data;

[0037] Figure 3 is a flow chart of a method for determining an update requirement type in an associated data type;

[0038] Figure 4 The present invention is a flowchart of a method for determining a real-time update data type among screening data types. DETAILED DESCRIPTION

[0039] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0040] In the present application, the update and processing strategy of the enterprise operation data is determined through the enterprise operation data of the ship repair enterprise and the number of related enterprise operation efficiency indicators. Specifically, the enterprise operation data with a large number of related enterprise operation efficiency indicators are updated and processed using Internet of Things monitoring equipment such as monitoring devices, and the enterprise operation data with a small number of related enterprise operation efficiency indicators are updated and processed using manual statistics, thereby ensuring the accuracy of the evaluation and processing of the enterprise operation efficiency indicators, and also avoiding the technical problem of greater difficulty in updating and processing caused by using Internet of Things monitoring equipment to update all enterprise operation data.

[0041] Example 1

[0042] like Figure 1 As shown, the present application provides a data analysis method for ship repair enterprise operation management, specifically including:

[0043] S1 determines, based on the data type of the enterprise operation data, a related maintenance process of the enterprise operation data in the ship maintenance process, and determines a screening data type in the data type of the enterprise operation data based on a ship maintenance plan and the related maintenance process within a preset future time period;

[0044] S2: taking data types that have a data association relationship with the screened data type as associated data types, and determining the update requirement type in the associated data types based on the associated business indicators of different associated data types;

[0045] S3: classifying the business indicators associated with the screened data type into the same business indicator group, and determining the real-time update data type in the screened data type based on the business indicators in the business indicator group and the constituent data of the update requirement type in the associated data type;

[0046] S4: obtaining data change information of a real-time update data type that has a data association relationship with the filter data type, and determining whether update processing of the filter data type is required based on the data change information.

[0047] Furthermore, the data types of the enterprise operation data include plate procurement data, plate collection data, welding rod procurement data, welding rod collection data, and water consumption.

[0048] Specifically, the associated maintenance process is a maintenance process in which the data type of the enterprise operation data changes during the maintenance process.

[0049] Specifically, such as Figure 2 As shown, the method for determining the screening data type in the data type of the enterprise operation data is:

[0050] Determining distribution data of associated maintenance processes of the data type within a future forecast period based on a ship maintenance processing plan within a future preset period;

[0051] Based on the distribution data of the associated maintenance process of the data type, the date on which the associated maintenance process exists is used as the associated date;

[0052] Whether the data type is a screening data type is determined based on the proportion of the number of associated dates within a future preset time period.

[0053] Furthermore, when the proportion of the number of associated dates in a future preset time period is greater than a preset date proportion threshold, the data type is determined to be a screening data type.

[0054] It should be noted that when the data type does not belong to the screened data type, the data update process of the data type is performed by manual statistics.

[0055] Optionally, the method for determining the filtered data type in the data type of the enterprise operation data is:

[0056] Determining distribution data of associated maintenance processes of the data type within a future forecast period based on a ship maintenance processing plan within a future preset period;

[0057] Based on the distribution data of the associated maintenance process of the data type, the time period in which the associated maintenance process exists is used as the associated time period;

[0058] Based on the number of associated time periods within a future preset time period, it is determined whether the data type is a screening data type.

[0059] Further, when the number of the associated time periods is greater than a preset threshold value of the number of associated time periods, it is determined that the data type is a screening data type.

[0060] Optionally, the method for determining the filtered data type in the data type of the enterprise operation data is:

[0061] Based on a ship maintenance processing plan within a future preset time period, determining distribution data of maintenance processes associated with the data type within a future forecast period, and determining that the data type does not belong to the screening data type when no date with the associated maintenance process exists based on the distribution data of maintenance processes associated with the data type;

[0062] When there is a date with the stated associated repair process:

[0063] The date on which the associated maintenance process exists is used as the associated date. When the number of associated dates in a future preset period accounts for more than the preset number of associated dates, it is determined that the data type belongs to the screening data type.

[0064] When the proportion of the number of associated dates in the future preset period is not greater than the proportion of the number of preset associated dates:

[0065] The time period in which the associated maintenance process exists is regarded as an associated time period, and when the number of associated time periods in a future preset time period is greater than the preset number of associated time periods, the data type is determined to be a screening data type;

[0066] When the number of associated periods in the future preset period is not greater than the preset number of associated periods:

[0067] When the date intervals between different associated dates are all greater than a preset date quantity threshold, it is determined that the data type does not belong to the screening data type;

[0068] When there is a related date whose interval with the adjacent related date is not greater than the preset date number threshold:

[0069] Determining association weight coefficients for different association dates based on the number and distribution data of association time periods in different association dates; and determining that the data type belongs to the screening data type when the association weight coefficients for different association dates are all greater than a preset association weight coefficient threshold;

[0070] When there is an associated date whose associated weight coefficient is not greater than the preset associated weight coefficient threshold:

[0071] The association clustering coefficient is determined based on the date intervals between different association dates and in combination with the association weight coefficients of different association dates, and the association clustering coefficient is used to determine whether the data type belongs to the screening data type.

[0072] Furthermore, when the associated clustering coefficient is greater than a preset clustering coefficient threshold, it is determined that the data type belongs to a screening data type.

[0073] It should be noted that the associated data type is a data type that has a certain association relationship with the data change of the screening data type.

[0074] Specifically, such as Figure 3 As shown, the method for determining the update requirement type in the associated data type is:

[0075] Taking the business indicator associated with the associated data type as the associated business indicator, and determining the update requirement coefficients of different associated business indicators based on the proportion of the number of the screening data types in different associated business indicators;

[0076] Based on the update demand coefficient of the associated business indicator, determining the associated business indicator whose update demand coefficient is greater than the preset update demand coefficient, and using it as the update business indicator;

[0077] According to the number of the update business indicators, it is determined whether the associated data type is an update requirement type.

[0078] Furthermore, the business indicators include cylinder maintenance volume, boiler maintenance volume, propeller maintenance volume and pipeline disassembly and assembly volume.

[0079] It can be understood that when the number of the update service indicators is greater than a preset update service indicator number threshold, it is determined that the associated data type is an update requirement type.

[0080] Optionally, the method for determining the update requirement type in the associated data type is:

[0081] Taking the business indicator associated with the associated data type as the associated business indicator, and determining the update requirement coefficients of different associated business indicators based on the proportion of the number of the screening data types in different associated business indicators;

[0082] Acquire the number of the associated business indicators, and determine the sum of update requirement coefficients based on the number of the associated business indicators and update requirement coefficients of different associated business indicators;

[0083] It is determined whether the associated data type is an update-required type according to the sum of the update-required coefficients.

[0084] Further, when the sum of the update requirement coefficients is greater than a preset requirement coefficient threshold, it is determined that the associated data type is an update requirement type.

[0085] Optionally, the method for determining the update requirement type in the associated data type is:

[0086] Taking the business indicators associated with the associated data type as associated business indicators, and when the number of the associated business indicators of the associated data type is greater than a preset number of business indicators, determining that the associated data type is an update requirement type;

[0087] When the number of associated business indicators of the associated data type is not greater than the preset number of business indicators:

[0088] Determining update requirement coefficients of different associated business indicators based on the proportion of the number of the filtered data types in different associated business indicators; when the update requirement coefficients of the different associated business indicators are all not greater than the preset update requirement coefficients, determining that the associated data type does not belong to the update requirement type;

[0089] When there is an associated business indicator whose update demand coefficient is greater than the preset update demand coefficient:

[0090] taking the associated business indicators whose update requirement coefficient is greater than the preset update requirement coefficient as update business indicators, and determining that the associated data type belongs to the update requirement type when the number of the update business indicators is greater than the preset update business indicator number threshold;

[0091] When the number of the updated service indicators is not greater than the preset threshold of the number of updated service indicators

[0092] Obtaining the number of the associated business indicators, determining a sum of update requirement coefficients based on the number of the associated business indicators and update requirement coefficients of different associated business indicators, and determining that the associated data type is an update requirement type when the sum of the update requirement coefficients is greater than a preset requirement coefficient threshold;

[0093] When the sum of the updated demand coefficients is not greater than the preset demand coefficient threshold:

[0094] The update requirement value of the associated data type is determined based on the number of different associated business indicators and the update requirement coefficients of different associated business indicators, and the update requirement value is used to determine whether the associated data type is an update requirement type.

[0095] Further, when the update requirement value is greater than a preset update requirement threshold, it is determined that the associated data type is an update requirement type.

[0096] Specifically, such as Figure 4 As shown, the method for determining the real-time update data type in the screening data type is:

[0097] Determining the quantity ratio of the update requirement types in the associated data type based on the constituent data of the update requirement types in the associated data type;

[0098] Determining the number of associations based on an average of the number of associated data types and the number of business indicators in the business indicator group;

[0099] The data requirement value of the filtered data type is determined based on the product of the association quantity and the proportion of the update requirement type in the associated data type, and whether the filtered data type is a real-time update data type is determined based on the data requirement value.

[0100] Further, when the data demand value of the filtered data type is greater than a preset demand threshold, it is determined that the filtered data type is a real-time update data type.

[0101] It should be noted that when the filtered data type is a real-time update data type, the Internet of Things monitoring device is used to perform real-time update processing of the data of the filtered data type.

[0102] Optionally, the method for determining the real-time update data type in the screening data type is:

[0103] S31 determines the basic data update requirement coefficient of the screening data type based on the number of business indicators in the business indicator group and the proportion of the screening data types in different business indicators;

[0104] S32 determines the proportion of the update requirement types in the associated data type based on the constituent data of the update requirement types in the associated data type, and determines the data type association coefficient in combination with the number of associated data types;

[0105] S33 determines the data requirement value of the filtered data type according to the sum of the data type association coefficient and the basic data update requirement coefficient, and determines whether the filtered data type is a real-time update data type based on the data requirement value.

[0106] Optionally, the above step S31 includes the following contents:

[0107] S311 obtains the number of business indicators in the business indicator group. When the number of business indicators in the business indicator group is greater than a preset indicator number threshold, determines that the screening data type is a real-time update data type. When the number of business indicators in the business indicator group is not greater than the preset indicator number threshold, proceeds to step S312.

[0108] S312: When the number of business indicators of the business indicator group is within the preset business indicator number range, the process proceeds to step S313; when the number of business indicators of the business indicator group is within the preset business indicator number range, the process proceeds to step S314;

[0109] S313: When the proportion of the number of the filtered data types of the business indicators in the business indicator group is less than the proportion of the number of the preset data types, it is determined that the filtered data type does not belong to the real-time update data type. When there is a business indicator in which the proportion of the number of the filtered data type is not less than the proportion of the number of the preset data type, the process proceeds to step S314.

[0110] S314 determines the basic data update requirement coefficient of the filtered data type based on the number of business indicators in the business indicator group and the proportion of the number of filtered data types in different business indicators. When the basic data update requirement coefficient of the filtered data type is greater than the preset data update requirement coefficient threshold, the filtered data type is determined to be a real-time update data type. When the basic data update requirement coefficient of the filtered data type is not greater than the preset data update requirement coefficient threshold, proceed to step S32.

[0111] Optionally, the above step S32 includes the following contents:

[0112] S321: Acquire the number of associated data types of the filtered data type. When the number of associated data types of the filtered data type is greater than a preset threshold value of the number of associated data types, determine that the filtered data type is a real-time update data type. When the number of associated data types of the filtered data type is not greater than the preset threshold value of the number of associated data types, proceed to step S322.

[0113] S322 determines the quantity ratio of the update requirement types in the associated data type based on the constituent data of the update requirement types in the associated data type. When the quantity ratio of the update requirement types in the associated data type is less than a preset quantity ratio threshold, the process proceeds to step S323. When the quantity ratio of the update requirement types in the associated data type is not less than the preset quantity ratio threshold, the process proceeds to step S324.

[0114] At step S323, when the number of associated data types of the filtered data type is within a preset associated data type number range, it is determined that the filtered data type does not belong to the real-time update data type; and when the number of associated data types of the filtered data type is not within the preset associated data type number range, the process proceeds to step S324.

[0115] S324 determines the data type association coefficient based on the proportion of the update requirement types in the associated data type and the number of associated data types. When the data type association coefficient is greater than the preset type association coefficient threshold, it is determined that the screened data type belongs to the real-time update data type. When the data type association coefficient is not greater than the preset type association coefficient threshold, proceed to step S33.

[0116] Furthermore, the real-time update data type having a data association relationship with the screening data type is a real-time update data type having a certain association relationship with data changes of the screening data type.

[0117] Specifically, determining whether to update the filtered data type includes:

[0118] Obtain data changes of real-time update data types that have a data association relationship with the filtered data type, and determine whether to update the filtered data type based on the data changes

[0119] The real-time update data type that has a data association relationship with the screening data type is set as the associated update data type;

[0120] Based on the data change of the associated updated data type, performing different data change amounts of the associated updated data type;

[0121] Whether the update process of the filtered data type needs to be performed is determined based on the data variation of different associated update data types.

[0122] Furthermore, determining whether the update process of the filtered data type is required based on the data variation of different associated updated data types specifically includes:

[0123] When there is an associated updated data type whose data variation is greater than a preset data variation, it is determined that the update process of the filtered data type needs to be performed.

[0124] It should be noted that, when it is determined that the filtered data type needs to be updated, the Internet of Things monitoring device is used to update the data of the filtered data type.

[0125] Example 2

[0126] In a second aspect, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned data analysis method for operation and management of a ship repair enterprise when running the computer program.

[0127] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0128] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A data analysis method for ship repair enterprise operation management, characterized in that: Specifically include: Determine, based on the data type of the enterprise operation data, the associated maintenance process of the enterprise operation data in the ship maintenance process, and determine, based on the ship maintenance plan and the associated maintenance process within a preset future time period, the filter data type within the data type of the enterprise operation data; Taking data types that have a data association relationship with the screened data type as associated data types, and determining update requirement types in the associated data types based on associated business indicators of different associated data types; Classifying the business indicators associated with the screening data type into the same business indicator group, and determining the real-time update data type in the screening data type based on the business indicators in the business indicator group and the constituent data of the update requirement type in the associated data type; A data change status of a real-time update data type that has a data association relationship with the filter data type is obtained, and whether update processing of the filter data type needs to be performed based on the data change status.

2. The data analysis method for ship repair enterprise operation management according to claim 1, characterized in that: The data types of the enterprise operation data include plate procurement data, plate collection data, welding rod procurement data, welding rod collection data, and water consumption.

3. The data analysis method for ship repair enterprise operation management according to claim 1, characterized in that: The associated maintenance process is a maintenance process in which the data type of the enterprise operation data changes during the maintenance process.

4. The data analysis method for ship repair enterprise operation management according to claim 1, characterized in that: The method for determining the screening data type in the data type of the enterprise operation data is as follows: Determining distribution data of associated maintenance processes of the data type within a future forecast period based on a ship maintenance processing plan within a future preset period; Based on the distribution data of the associated maintenance process of the data type, the date on which the associated maintenance process exists is used as the associated date; Whether the data type is a screening data type is determined based on the proportion of the number of associated dates within a future preset time period.

5. The data analysis method for ship repair enterprise operation management according to claim 4, characterized in that: When the proportion of the number of associated dates in a future preset time period is greater than a preset date proportion threshold, the data type is determined to be a screening data type.

6. The data analysis method for ship repair enterprise operation management according to claim 1, characterized in that: When the data type does not belong to the screening data type, data update processing of the data type is performed by manual statistics.

7. The data analysis method for ship repair enterprise operation management according to claim 1, characterized in that: Determining whether to update the filtered data type specifically includes: The real-time update data type that has a data association relationship with the screening data type is set as the associated update data type; Based on the data change of the associated updated data type, performing different data change amounts of the associated updated data type; Whether the update process of the filtered data type needs to be performed is determined based on the data variation of different associated update data types.

8. The data analysis method for ship repair enterprise operation management according to claim 7, characterized in that: Determining whether to perform update processing on the filtered data type based on the data change amounts of different associated updated data types specifically includes: When there is an associated updated data type whose data variation is greater than a preset data variation, it is determined that the update process of the filtered data type needs to be performed.

9. The data analysis method for ship repair enterprise operation management according to claim 7, characterized in that: When it is determined that the filtered data type needs to be updated, the Internet of Things monitoring device is used to update the data of the filtered data type.

10. A computer system comprising: A memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a data analysis method for operation management of a ship repair enterprise as described in any one of claims 1 to 9 is executed.

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